A Comparative Study of Tensor Networks and Traditional Neural Networks
Abstract
Tensor networks with various structures have been proposed as alternatives to traditional neural networks. Tensor networks represent low-rank factorizations of traditional neural network kernels and can be evaluated using fewer arithmetic operations. However, higher performance is not guaranteed since the achievable fraction of machine peak for evaluation of tensor networks with many small tensors can be considerably lower than that attained in evaluating traditional neural network kernels. This project will preform a comparative evaluation of currently achievable performance on multicore CPUs as well as GPUs, using traditional kernels versus tensor networks. a number of popular machine learning frameworks like TensorFlow and PyTorch will be evaluated, as well as stand-alone code generators and libraries for tensor computations.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Sep 04, 2019
- Source ID
- W911NF1910491
Entities
People
- Ponnuswamy Sadayappan
Organizations
- Army Contracting Command
- National Security Agency
- University of Utah